Physical–Chemical Properties of Biogenic Selenium Nanostructures Produced by Stenotrophomonas maltophilia SeITE02 and Ochrobactrum sp. MPV1
Bibliographic record
Abstract
Stenotrophomonas maltophilia SeITE02 and Ochrobactrum sp. MPV1 were isolated from the rhizosphere soil of the selenium-hyperaccumulator legume Astragalus bisulcatus and waste material from a dumping site for roasted pyrites, respectively. Here, these bacterial strains were studied as cell factories to generate selenium-nanostructures (SeNS) under metabolically controlled growth conditions. Thus, a defined medium (DM) containing either glucose or pyruvate as carbon and energy source along with selenite (SeO32-) was tested to evaluate bacterial growth, oxyanion bioconversion and changes occurring in SeNS features with respect to those generated by these strains grown on rich media. Transmission Electron Microscopy (TEM) images show extra- or intra-cellular emergence of SeNS in SeITE02 or MPV1 respectively, revealing the presence of two distinct biological routes of SeNS biogenesis. Indeed, the stress exerted by SeO32- upon SeITE02 cells triggered the production of membrane vesicles (MVs), which surrounded Se-nanoparticles (SeNPs), as highlighted by TEM and Scanning Electron Microscopy (SEM), strongly suggesting that MVs might play a crucial role in the excreting mechanism of the SeNPs in the extracellular environment. On the other hand, MPV1 strain biosynthesized intracellular inclusions likely containing hydrophobic storage compounds and SeNPs under pyruvate conditioning, while the growth on glucose as the only source of carbon and energy led to the production of a mixed population of intracellular SeNPs and nanorods (SeNRs). SEM, fluorescence spectroscopy, and Confocal Laser Scanning Microscopy (CLSM) revealed that the biogenic SeNS were enclosed in an organic material containing proteins and amphiphilic molecules, possibly responsible for the high thermodynamic stability of these nanomaterials. Finally, the biogenic SeNS extracts were photoluminescent, whose degree of fluorescence emission was comparable to that from chemically synthesized SeNPs with L-cysteine (L-cys SeNPs). This study offers novel insights into the formation, localization, and release of biogenic SeNS generated by two different Gram-negative bacterial strains under aerobic and metabolically controlled growth conditions. The work strengthens the possibility of using these bacterial isolates as eco-friendly biocatalysts to produce high quality SeNS targeted to possible biomedical applications and other biotechnological purposes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".